Gait recognition invariant to carried objects using alpha blending generative adversarial networks. (September 2020)

• Record Type: Journal Article
• Authors: Li, Xiang; Makihara, Yasushi; Xu, Chi; Yagi, Yasushi; Ren, Mingwu
• Is Part Of: Pattern recognition. Volume 105(2020:Sep.)
• Publication Date: 2020-09
• Subjects: Alpha blending -- Generative adversarial network -- Gait recognition -- Carried objects; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107376
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: End-to-end framework for gait recognition invariant to carried objects. Alpha blending generative adversarial network for removing carried objects. State-of-the-art accuracy for gait recognition under various carrying objects. Abstract: Gait recognition invariant to carried objects (COs) is very difficult in a real-life scene because the COs can have various shapes and sizes, in addition to unpredictable carrying locations (e.g., front, back, and side, or multiple locations). Therefore, in this paper, we propose a robust method for gait recognition against various COs by reconstructing a gait template without COs. A straightforward approach is to directly generate a gait template without COs given a gait template with COs as the input using a conventional generative adversarial network. There is, however, a potential risk of unnecessarily altering parts that were originally unaffected by COs (e.g., leg parts for a person carrying a backpack). Because we do not want to touch such unaffected parts in the original template, we first estimate a gait template without COs, and then blend it with the original template by an estimated alpha matte that indicates the blending parameters. We then create an alpha-blended template from the original template and the generated template without COs based on the estimated alpha matte. We use two independent generators to estimate the alpha matte and the generated template without COs. Finally, we feed the alpha-blended gait template into a state-of-the-art discrimination network for gait recognition. The experimental results on three publicly available gait databases with real-life COs demonstrate the state-of-the-art performance of the proposed method.